Overview

Data available through: 2020-06-28

Total Cases and Total Deaths

These show the cumulative total of cases and deaths by day. I have also denoted the current cumulative totals. These total values are important; however they are not helpful for figuring out whether the pandemic is slowing down or growing as it is difficult to see trends in cumulative curves like these.


New Cases

Looking at new cases each day can help us see if the pandemic is slowing. A decreasing number of new cases per day is evidence that the pandemic is slowing down.

There can be a lot of variability in the daily case totals due to a variety of variables. One example is the availability of tests; cases will go down if there is a scarcity of tests and rise dramatically when more tests become available. One way to help get a better sense of the overall trend is by smoothing the data using a moving average.

The trends and raw data show a peak around mid-April and had been moving downward likely due to strict lock-down and social distancing measures. In late June, new cases started to increase dramatically, forming a second peak as many states start re-opening and many people stop practicing social distancing measures. There is also a cyclical nature to the daily new cases with counts often being lower on weekends and higher on weekdays.


New Deaths and Death Percentage

COVID-19 is much deadlier than the common flu. One way to measure the impact is to look at the death percentage, which is the total number of deaths divided by the total number of cases.

A big concern during April was that the death percentage was continually increasing, even when actual deaths per day were not increasing. Starting in early May the death percentage started to plateau around 6%. At the end of may, the death percentage starts curving downward as deaths per day continued to decrease as new cases per days continued to plateau and then increase.

Similar to new cases there is a cyclical nature to spikes in new cases. These spikes may be due to reporting times where counts on weekdays are often higher than those on weekends. Spikes also occur when previous deaths are re-assigned as COVID-19 related deaths, such as counting nursing home deaths and/or pneumonia deaths.


Values for Past 14 Days

The actual values for the previous 14 days are detailed in the table below.

Date Total Cases Total Deaths New Cases New Cases 7-Day MA New Deaths New Deaths 7-Day MA Death Percentage
Sun, Jun 28, 2020 2,532,801 125,008 39,320 38,033 272 837 4.936%
Sat, Jun 27, 2020 2,493,481 124,736 42,300 36,254 2,387 836 5.002%
Fri, Jun 26, 2020 2,451,181 122,349 45,806 34,742 639 576 4.991%
Thu, Jun 25, 2020 2,405,375 121,710 39,084 32,618 620 591 5.060%
Wed, Jun 24, 2020 2,366,291 121,090 35,407 30,916 753 603 5.117%
Tue, Jun 23, 2020 2,330,884 120,337 34,944 29,350 827 607 5.163%
Mon, Jun 22, 2020 2,295,940 119,510 29,374 27,858 367 599 5.205%
Sun, Jun 21, 2020 2,266,566 119,143 26,863 26,316 265 605 5.257%
Sat, Jun 20, 2020 2,239,703 118,878 31,721 25,402 562 615 5.308%
Fri, Jun 19, 2020 2,207,982 118,316 30,938 24,546 749 639 5.359%
Thu, Jun 18, 2020 2,177,044 117,567 27,171 23,798 699 642 5.400%
Wed, Jun 17, 2020 2,149,873 116,868 24,441 23,133 785 667 5.436%
Tue, Jun 16, 2020 2,125,432 116,083 24,502 22,562 771 684 5.462%
Mon, Jun 15, 2020 2,100,930 115,312 18,579 21,716 404 712 5.489%

Growth Factor

Data available through: 2020-06-28

Is the pandemic slowing?

One important calculation is the growth factor, as outlined in 3Blue1Brown’s youtube video on exponential growth . The growth factor is calculated as follows:

\[ \text{Growth Factor} = \frac{ \text{New-Cases}_N}{\text{New-Cases}_{N-1}} \] where \(N\) is a given day. Essentialy this is taking the amount of new cases today and dividing them by the amount of new cases yesterday.

The growth factor can be very helpful in determining if the pandemic is slowing. If the growth factor is less than 1, this means that the amount of new cases today is less than yesterday. Once there are multiple days with a growth factor less than 1 it is a strong sign that the pandemic is slowing down.

Adjustment to Growth Factor

What if there were 0 cases yesterday? This would make the growth factor undefined (or \(\infty\) according to R). This makes it difficult to look at trends. I have adjusted the growth factor so that if the previous day had 0 cases, the current day’s growth factor is equal to the number of new cases:

\[ \text{Growth Factor} = \begin{cases} \frac{ \text{New-Cases}_N}{\text{New-Cases}_{N-1}} & \text{if } \text{New-Cases}_{N-1} \neq 0 \\[1ex] \text{New-Cases}_N & \text{if } \text{New-Cases}_{N-1} = 0 \end{cases} \] I made this adjustment for the early or late stages of the pandemic when the number of cases per day are 0, 1, or 2. However, given the test scarcity and reporting times there are situations in counties or states where there are 0 cases one day and then hundreds or thousands the next day. This large variability causes spikes in the growth factor in some plots.

Growth Factor Plot

Similar to the new cases per day, there can be a lot of variability in growth factors In order to get a better sense of the trend I am showing a 14-day moving average of the growth factor.

The growth factor shows a different trend than new cases. Here, the growth factor has stayed around 1 since mid-April. Compare that to the new cases plot on the Overview tab, which shows a downward trend after a peak in mid-April. The growth factor remaining around 1 may be due to the cyclical nature of new cases being reported (high during the week, low during the weekends) - but it could also be showing that although the decrease in new cases is a positive sign, we are not out of the woods yet.


Recent Values by Day for US

The actual values for the previous 14 days are detailed in the table below.

Date Total Cases New Cases New Cases 7-Day MA Growth Factor Growth Factor 7-day MA
Sun, Jun 28, 2020 2,532,801 39,320 38,033 0.93 1.06
Sat, Jun 27, 2020 2,493,481 42,300 36,254 0.92 1.05
Fri, Jun 26, 2020 2,451,181 45,806 34,742 1.17 1.06
Thu, Jun 25, 2020 2,405,375 39,084 32,618 1.1 1.06
Wed, Jun 24, 2020 2,366,291 35,407 30,916 1.01 1.06
Tue, Jun 23, 2020 2,330,884 34,944 29,350 1.19 1.06
Mon, Jun 22, 2020 2,295,940 29,374 27,858 1.09 1.08
Sun, Jun 21, 2020 2,266,566 26,863 26,316 0.85 1.05
Sat, Jun 20, 2020 2,239,703 31,721 25,402 1.03 1.04
Fri, Jun 19, 2020 2,207,982 30,938 24,546 1.14 1.04
Thu, Jun 18, 2020 2,177,044 27,171 23,798 1.11 1.04
Wed, Jun 17, 2020 2,149,873 24,441 23,133 1 1.04
Tue, Jun 16, 2020 2,125,432 24,502 22,562 1.32 1.05
Mon, Jun 15, 2020 2,100,930 18,579 21,716 0.91 1.02

State Plots

Data available through: 2020-06-28

Case Curves by State



Percentage Change of Weekly Average from Week to Week

These plots are based on NPR’s plots using percentage change in new cases. I also used the same colors. NPR plots can be found here.



Recent Growth Factor by State

Since most of the COVID-19 measures are enacted by individual states, it may be more helpful for an individual to see the growth factor for the last 14 days in a specific state.


Data

Data available through: 2020-06-28

This data is downloaded from USA Facts. I use two of the three datasets available: total cases and total deaths. Both of these datasets are broken down by state and county. This data requires additional formatting, calculation, and aggregation. USA Facts gets data by county on a daily basis, this is totaled to get values for each day for individual states and the entire US.

The American CDC links to USA Facts under Cases & Death by County, which is how I found the data source.

Many of the plots have been restricted to show data on March 15, 2020 and after. This is when case numbers started to rise and preventative measures started to increase dramatically.

Data Limitations

A large limitation for this data is that reported new cases (and thus the growth factor) may not consistently and accurately represent the true number of new cases each day. As mentioned before, this could be due to test availability, reporting protocols, and a number of other variables. It is important to note that this information is a helpful tool in trying to understand the pandemic, but it may not reflect the entire story.